ExtreMA enjoys the favorable properties of Siamese representation learning. The instance representation from the model can be used off-the-shelf for measuring semantic similarities.
The framework also welcomes other data augmentations besides masking for applications with different ends.
However, unlike typical contrastive learning [1]}, [2]}, [3]}, ExtreMA does not rely on data augmentation induced invariances to achieve generalization.
Rather, ExtreMA
preserves all possible useful information from the masked view in order to recover the full.
The generative aspects of our model are exhibited in Figure REF . It can faithfully inpaint the masked pixels through network inversion [4]}. Moreover, the instance representation is also shown to be sensitive to spatial and scale variations for localizing objects in Figure REF .
These properties demonstrates that ExtreMA learns both instance and distributed representations that well captures scale, location, and color intensities.
